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Related Experiment Video

Updated: Jun 9, 2025

Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping
09:48

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Published on: November 7, 2016

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High-Resolution Reconstruction of Temperature Fields Based on Improved ResNet18.

Leilei Ma1, Jungang Ma1, Manlidan Zelminbek1

  • 1Xinjiang Uygur Autonomous Region Research Institute Of Measurement & Testing, Urumqi 830011, China.

Sensors (Basel, Switzerland)
|October 26, 2024
PubMed
Summary

This study introduces a novel deep learning algorithm for high-precision temperature field reconstruction in industrial settings. The enhanced method significantly improves accuracy over traditional approaches, benefiting industrial production.

Keywords:
deep learningmean square errormulti-scale feature aggregationtemperature field reconstruction

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Area of Science:

  • Industrial Engineering
  • Computer Science
  • Applied Mathematics

Background:

  • Accurate temperature field measurement is crucial for industrial production.
  • Traditional reconstruction algorithms suffer from high computational costs and limited generalization.

Purpose of the Study:

  • To develop a high-precision temperature field reconstruction algorithm using deep learning.
  • To overcome limitations of traditional manual feature extraction methods.

Main Methods:

  • An improved ResNet18 neural network incorporating a CBAM attention mechanism.
  • A multi-scale feature aggregation network (M-FPN) for enhanced feature propagation.
  • Mean Squared Error (MSE) for guided model optimization.

Main Results:

  • The proposed deep learning algorithm achieves significantly higher reconstruction accuracy.
  • The model demonstrates superior performance compared to original algorithms on peaked temperature fields.
  • Adaptive feature extraction improves computational efficiency and generalization.

Conclusions:

  • The developed deep learning algorithm offers a superior solution for industrial temperature field reconstruction.
  • The integration of attention mechanisms and multi-scale feature fusion enhances predictive capabilities.
  • This approach provides a more accurate and efficient tool for industrial monitoring and control.